Overview

Dataset statistics

Number of variables31
Number of observations570394
Missing cells2992834
Missing cells (%)16.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory134.9 MiB
Average record size in memory248.0 B

Variable types

Numeric20
Categorical5
Text6

Alerts

CANCELLED is highly imbalanced (96.2%)Imbalance
DIVERTED is highly imbalanced (97.9%)Imbalance
CANCELLATION_CODE has 568084 (99.6%) missing valuesMissing
CARRIER_DELAY has 481241 (84.4%) missing valuesMissing
WEATHER_DELAY has 481241 (84.4%) missing valuesMissing
NAS_DELAY has 481241 (84.4%) missing valuesMissing
SECURITY_DELAY has 481241 (84.4%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 481241 (84.4%) missing valuesMissing
SECURITY_DELAY is highly skewed (γ1 = 34.0490753)Skewed
DEP_DELAY has 27576 (4.8%) zerosZeros
ARR_DELAY has 10088 (1.8%) zerosZeros
CARRIER_DELAY has 39145 (6.9%) zerosZeros
WEATHER_DELAY has 85878 (15.1%) zerosZeros
NAS_DELAY has 44910 (7.9%) zerosZeros
SECURITY_DELAY has 88394 (15.5%) zerosZeros
LATE_AIRCRAFT_DELAY has 45982 (8.1%) zerosZeros

Reproduction

Analysis started2024-03-30 06:11:48.350749
Analysis finished2024-03-30 06:14:44.268681
Duration2 minutes and 55.92 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1730979
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:44.375413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9879171
Coefficient of variation (CV)0.47636485
Kurtosis-1.2135648
Mean4.1730979
Median Absolute Deviation (MAD)2
Skewness-0.13505156
Sum2380310
Variance3.9518145
MonotonicityIncreasing
2024-03-30T03:14:44.609283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 97191
17.0%
7 89536
15.7%
6 85997
15.1%
4 77853
13.6%
3 73891
13.0%
1 73316
12.9%
2 72610
12.7%
ValueCountFrequency (%)
1 73316
12.9%
2 72610
12.7%
3 73891
13.0%
4 77853
13.6%
5 97191
17.0%
6 85997
15.1%
7 89536
15.7%
ValueCountFrequency (%)
7 89536
15.7%
6 85997
15.1%
5 97191
17.0%
4 77853
13.6%
3 73891
13.0%
2 72610
12.7%
1 73316
12.9%

FL_DATE
Categorical

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
12/22/2023 12:00:00 AM
 
19840
12/21/2023 12:00:00 AM
 
19752
12/15/2023 12:00:00 AM
 
19558
12/14/2023 12:00:00 AM
 
19484
12/7/2023 12:00:00 AM
 
19432
Other values (26)
472328 

Length

Max length22
Median length22
Mean length21.713314
Min length21

Characters and Unicode

Total characters12385144
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12/4/2023 12:00:00 AM
2nd row12/4/2023 12:00:00 AM
3rd row12/4/2023 12:00:00 AM
4th row12/4/2023 12:00:00 AM
5th row12/4/2023 12:00:00 AM

Common Values

ValueCountFrequency (%)
12/22/2023 12:00:00 AM 19840
 
3.5%
12/21/2023 12:00:00 AM 19752
 
3.5%
12/15/2023 12:00:00 AM 19558
 
3.4%
12/14/2023 12:00:00 AM 19484
 
3.4%
12/7/2023 12:00:00 AM 19432
 
3.4%
12/8/2023 12:00:00 AM 19415
 
3.4%
12/29/2023 12:00:00 AM 19283
 
3.4%
12/26/2023 12:00:00 AM 19207
 
3.4%
12/11/2023 12:00:00 AM 19206
 
3.4%
12/10/2023 12:00:00 AM 19195
 
3.4%
Other values (21) 376022
65.9%

Length

2024-03-30T03:14:44.919018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00:00 570394
33.3%
am 570394
33.3%
12/22/2023 19840
 
1.2%
12/21/2023 19752
 
1.2%
12/15/2023 19558
 
1.1%
12/14/2023 19484
 
1.1%
12/7/2023 19432
 
1.1%
12/8/2023 19415
 
1.1%
12/29/2023 19283
 
1.1%
12/26/2023 19207
 
1.1%
Other values (23) 414423
24.2%

Most occurring characters

ValueCountFrequency (%)
0 2908740
23.5%
2 2521869
20.4%
1 1400691
11.3%
/ 1140788
 
9.2%
1140788
 
9.2%
: 1140788
 
9.2%
3 661379
 
5.3%
A 570394
 
4.6%
M 570394
 
4.6%
8 57276
 
0.5%
Other values (5) 272037
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12385144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2908740
23.5%
2 2521869
20.4%
1 1400691
11.3%
/ 1140788
 
9.2%
1140788
 
9.2%
: 1140788
 
9.2%
3 661379
 
5.3%
A 570394
 
4.6%
M 570394
 
4.6%
8 57276
 
0.5%
Other values (5) 272037
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12385144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2908740
23.5%
2 2521869
20.4%
1 1400691
11.3%
/ 1140788
 
9.2%
1140788
 
9.2%
: 1140788
 
9.2%
3 661379
 
5.3%
A 570394
 
4.6%
M 570394
 
4.6%
8 57276
 
0.5%
Other values (5) 272037
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12385144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2908740
23.5%
2 2521869
20.4%
1 1400691
11.3%
/ 1140788
 
9.2%
1140788
 
9.2%
: 1140788
 
9.2%
3 661379
 
5.3%
A 570394
 
4.6%
M 570394
 
4.6%
8 57276
 
0.5%
Other values (5) 272037
 
2.2%
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
WN
127773 
DL
81022 
AA
75408 
UA
59844 
OO
55996 
Other values (10)
170351 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1140788
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 127773
22.4%
DL 81022
14.2%
AA 75408
13.2%
UA 59844
10.5%
OO 55996
9.8%
NK 22583
 
4.0%
YX 21641
 
3.8%
B6 21398
 
3.8%
MQ 20420
 
3.6%
AS 19400
 
3.4%
Other values (5) 64909
11.4%

Length

2024-03-30T03:14:45.164410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 127773
22.4%
dl 81022
14.2%
aa 75408
13.2%
ua 59844
10.5%
oo 55996
9.8%
nk 22583
 
4.0%
yx 21641
 
3.8%
b6 21398
 
3.8%
mq 20420
 
3.6%
as 19400
 
3.4%
Other values (5) 64909
11.4%

Most occurring characters

ValueCountFrequency (%)
A 236762
20.8%
N 150356
13.2%
O 128133
11.2%
W 127773
11.2%
D 81022
 
7.1%
L 81022
 
7.1%
U 59844
 
5.2%
9 32586
 
2.9%
H 22843
 
2.0%
K 22583
 
2.0%
Other values (11) 197864
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1140788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 236762
20.8%
N 150356
13.2%
O 128133
11.2%
W 127773
11.2%
D 81022
 
7.1%
L 81022
 
7.1%
U 59844
 
5.2%
9 32586
 
2.9%
H 22843
 
2.0%
K 22583
 
2.0%
Other values (11) 197864
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1140788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 236762
20.8%
N 150356
13.2%
O 128133
11.2%
W 127773
11.2%
D 81022
 
7.1%
L 81022
 
7.1%
U 59844
 
5.2%
9 32586
 
2.9%
H 22843
 
2.0%
K 22583
 
2.0%
Other values (11) 197864
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1140788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 236762
20.8%
N 150356
13.2%
O 128133
11.2%
W 127773
11.2%
D 81022
 
7.1%
L 81022
 
7.1%
U 59844
 
5.2%
9 32586
 
2.9%
H 22843
 
2.0%
K 22583
 
2.0%
Other values (11) 197864
17.3%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5873
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2289.1566
Minimum1
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:45.607714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile282
Q11046
median2043
Q33343
95-th percentile5364
Maximum8819
Range8818
Interquartile range (IQR)2297

Descriptive statistics

Standard deviation1558.49
Coefficient of variation (CV)0.68081406
Kurtosis-0.55132119
Mean2289.1566
Median Absolute Deviation (MAD)1099
Skewness0.61889059
Sum1.3057212 × 109
Variance2428891.1
MonotonicityNot monotonic
2024-03-30T03:14:45.927048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
540 319
 
0.1%
323 306
 
0.1%
706 303
 
0.1%
533 301
 
0.1%
1201 301
 
0.1%
1744 300
 
0.1%
421 297
 
0.1%
1173 296
 
0.1%
649 294
 
0.1%
1245 293
 
0.1%
Other values (5863) 567384
99.5%
ValueCountFrequency (%)
1 164
< 0.1%
2 131
< 0.1%
3 119
< 0.1%
4 164
< 0.1%
5 136
< 0.1%
6 95
< 0.1%
7 116
< 0.1%
8 165
< 0.1%
9 145
< 0.1%
10 101
< 0.1%
ValueCountFrequency (%)
8819 5
< 0.1%
8810 1
 
< 0.1%
8806 1
 
< 0.1%
8805 1
 
< 0.1%
8804 2
 
< 0.1%
8803 1
 
< 0.1%
8802 2
 
< 0.1%
8801 1
 
< 0.1%
8800 8
< 0.1%
8799 1
 
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12659.526
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:46.266043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314057
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2765

Descriptive statistics

Standard deviation1530.9803
Coefficient of variation (CV)0.12093504
Kurtosis-1.2999836
Mean12659.526
Median Absolute Deviation (MAD)1591
Skewness0.099593906
Sum7.2209178 × 109
Variance2343900.7
MonotonicityNot monotonic
2024-03-30T03:14:46.606993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 28027
 
4.9%
11292 25054
 
4.4%
11298 23714
 
4.2%
13930 20664
 
3.6%
11057 16220
 
2.8%
14107 15996
 
2.8%
12892 15948
 
2.8%
12889 15872
 
2.8%
13204 15112
 
2.6%
14747 12736
 
2.2%
Other values (324) 381051
66.8%
ValueCountFrequency (%)
10135 376
 
0.1%
10136 149
 
< 0.1%
10140 2034
0.4%
10141 60
 
< 0.1%
10146 62
 
< 0.1%
10155 92
 
< 0.1%
10157 155
 
< 0.1%
10158 307
 
0.1%
10165 9
 
< 0.1%
10170 50
 
< 0.1%
ValueCountFrequency (%)
16869 152
 
< 0.1%
16218 137
 
< 0.1%
15991 60
 
< 0.1%
15919 997
0.2%
15841 60
 
< 0.1%
15624 599
0.1%
15607 62
 
< 0.1%
15582 51
 
< 0.1%
15569 51
 
< 0.1%
15412 1147
0.2%

ORIGIN
Text

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:47.303957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1711182
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLGA
2nd rowLGA
3rd rowTYS
4th rowLGA
5th rowLGA
ValueCountFrequency (%)
atl 28027
 
4.9%
den 25054
 
4.4%
dfw 23714
 
4.2%
ord 20664
 
3.6%
clt 16220
 
2.8%
phx 15996
 
2.8%
lax 15948
 
2.8%
las 15872
 
2.8%
mco 15112
 
2.6%
sea 12736
 
2.2%
Other values (324) 381051
66.8%
2024-03-30T03:14:48.297418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 194744
 
11.4%
L 158553
 
9.3%
S 146264
 
8.5%
D 132285
 
7.7%
T 91003
 
5.3%
O 86878
 
5.1%
C 85813
 
5.0%
M 78312
 
4.6%
F 70441
 
4.1%
N 66826
 
3.9%
Other values (16) 600063
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 194744
 
11.4%
L 158553
 
9.3%
S 146264
 
8.5%
D 132285
 
7.7%
T 91003
 
5.3%
O 86878
 
5.1%
C 85813
 
5.0%
M 78312
 
4.6%
F 70441
 
4.1%
N 66826
 
3.9%
Other values (16) 600063
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 194744
 
11.4%
L 158553
 
9.3%
S 146264
 
8.5%
D 132285
 
7.7%
T 91003
 
5.3%
O 86878
 
5.1%
C 85813
 
5.0%
M 78312
 
4.6%
F 70441
 
4.1%
N 66826
 
3.9%
Other values (16) 600063
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 194744
 
11.4%
L 158553
 
9.3%
S 146264
 
8.5%
D 132285
 
7.7%
T 91003
 
5.3%
O 86878
 
5.1%
C 85813
 
5.0%
M 78312
 
4.6%
F 70441
 
4.1%
N 66826
 
3.9%
Other values (16) 600063
35.1%
Distinct328
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:48.804433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.05763
Min length8

Characters and Unicode

Total characters7447994
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowNew York, NY
3rd rowKnoxville, TN
4th rowNew York, NY
5th rowNew York, NY
ValueCountFrequency (%)
ca 61792
 
4.6%
tx 60822
 
4.6%
fl 56986
 
4.3%
san 30263
 
2.3%
ga 29881
 
2.2%
ny 29364
 
2.2%
il 28539
 
2.1%
co 28155
 
2.1%
atlanta 28027
 
2.1%
chicago 27500
 
2.1%
Other values (399) 950779
71.4%
2024-03-30T03:14:49.854697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
761714
 
10.2%
a 572368
 
7.7%
, 570394
 
7.7%
o 407531
 
5.5%
e 394504
 
5.3%
n 365633
 
4.9%
t 352686
 
4.7%
l 329837
 
4.4%
i 281858
 
3.8%
r 270350
 
3.6%
Other values (46) 3141119
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7447994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
761714
 
10.2%
a 572368
 
7.7%
, 570394
 
7.7%
o 407531
 
5.5%
e 394504
 
5.3%
n 365633
 
4.9%
t 352686
 
4.7%
l 329837
 
4.4%
i 281858
 
3.8%
r 270350
 
3.6%
Other values (46) 3141119
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7447994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
761714
 
10.2%
a 572368
 
7.7%
, 570394
 
7.7%
o 407531
 
5.5%
e 394504
 
5.3%
n 365633
 
4.9%
t 352686
 
4.7%
l 329837
 
4.4%
i 281858
 
3.8%
r 270350
 
3.6%
Other values (46) 3141119
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7447994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
761714
 
10.2%
a 572368
 
7.7%
, 570394
 
7.7%
o 407531
 
5.5%
e 394504
 
5.3%
n 365633
 
4.9%
t 352686
 
4.7%
l 329837
 
4.4%
i 281858
 
3.8%
r 270350
 
3.6%
Other values (46) 3141119
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:50.392883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1137091
Min length4

Characters and Unicode

Total characters4628011
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNew York
3rd rowTennessee
4th rowNew York
5th rowNew York
ValueCountFrequency (%)
california 61792
 
9.5%
texas 60822
 
9.3%
florida 56986
 
8.8%
new 43138
 
6.6%
georgia 29881
 
4.6%
york 29364
 
4.5%
illinois 28539
 
4.4%
colorado 28155
 
4.3%
carolina 28132
 
4.3%
north 25050
 
3.8%
Other values (51) 258836
39.8%
2024-03-30T03:14:51.189054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 626453
13.5%
i 521790
 
11.3%
o 452661
 
9.8%
r 342481
 
7.4%
n 336183
 
7.3%
e 278712
 
6.0%
l 262140
 
5.7%
s 256941
 
5.6%
d 121057
 
2.6%
C 119750
 
2.6%
Other values (37) 1309843
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4628011
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 626453
13.5%
i 521790
 
11.3%
o 452661
 
9.8%
r 342481
 
7.4%
n 336183
 
7.3%
e 278712
 
6.0%
l 262140
 
5.7%
s 256941
 
5.6%
d 121057
 
2.6%
C 119750
 
2.6%
Other values (37) 1309843
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4628011
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 626453
13.5%
i 521790
 
11.3%
o 452661
 
9.8%
r 342481
 
7.4%
n 336183
 
7.3%
e 278712
 
6.0%
l 262140
 
5.7%
s 256941
 
5.6%
d 121057
 
2.6%
C 119750
 
2.6%
Other values (37) 1309843
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4628011
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 626453
13.5%
i 521790
 
11.3%
o 452661
 
9.8%
r 342481
 
7.4%
n 336183
 
7.3%
e 278712
 
6.0%
l 262140
 
5.7%
s 256941
 
5.6%
d 121057
 
2.6%
C 119750
 
2.6%
Other values (37) 1309843
28.3%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.892013
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:51.586844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median51
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.668044
Coefficient of variation (CV)0.4858274
Kurtosis-1.3177813
Mean54.892013
Median Absolute Deviation (MAD)23
Skewness-0.043781722
Sum31310075
Variance711.18458
MonotonicityNot monotonic
2024-03-30T03:14:51.956741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 61792
 
10.8%
74 60822
 
10.7%
33 56986
 
10.0%
34 29881
 
5.2%
22 29364
 
5.1%
41 28539
 
5.0%
82 28155
 
4.9%
36 23592
 
4.1%
38 18587
 
3.3%
81 18418
 
3.2%
Other values (42) 214258
37.6%
ValueCountFrequency (%)
1 2476
 
0.4%
2 11108
1.9%
3 3401
 
0.6%
4 664
 
0.1%
5 102
 
< 0.1%
11 1671
 
0.3%
12 893
 
0.2%
13 10756
1.9%
14 489
 
0.1%
15 1081
 
0.2%
ValueCountFrequency (%)
93 14966
 
2.6%
92 6337
 
1.1%
91 61792
10.8%
88 816
 
0.1%
87 9827
 
1.7%
86 2220
 
0.4%
85 17543
 
3.1%
84 2009
 
0.4%
83 2363
 
0.4%
82 28155
4.9%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12659.658
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:52.370122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314057
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2765

Descriptive statistics

Standard deviation1531.0627
Coefficient of variation (CV)0.12094029
Kurtosis-1.3000314
Mean12659.658
Median Absolute Deviation (MAD)1591
Skewness0.099440234
Sum7.2209931 × 109
Variance2344153
MonotonicityNot monotonic
2024-03-30T03:14:52.787586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 28052
 
4.9%
11292 25057
 
4.4%
11298 23680
 
4.2%
13930 20661
 
3.6%
11057 16213
 
2.8%
14107 16002
 
2.8%
12892 15955
 
2.8%
12889 15872
 
2.8%
13204 15125
 
2.7%
14747 12726
 
2.2%
Other values (324) 381051
66.8%
ValueCountFrequency (%)
10135 376
 
0.1%
10136 149
 
< 0.1%
10140 2033
0.4%
10141 60
 
< 0.1%
10146 62
 
< 0.1%
10155 92
 
< 0.1%
10157 155
 
< 0.1%
10158 306
 
0.1%
10165 9
 
< 0.1%
10170 49
 
< 0.1%
ValueCountFrequency (%)
16869 152
 
< 0.1%
16218 137
 
< 0.1%
15991 60
 
< 0.1%
15919 999
0.2%
15841 60
 
< 0.1%
15624 599
0.1%
15607 62
 
< 0.1%
15582 51
 
< 0.1%
15569 51
 
< 0.1%
15412 1147
0.2%

DEST
Text

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:53.575550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1711182
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTL
2nd rowSYR
3rd rowLGA
4th rowTYS
5th rowORF
ValueCountFrequency (%)
atl 28052
 
4.9%
den 25057
 
4.4%
dfw 23680
 
4.2%
ord 20661
 
3.6%
clt 16213
 
2.8%
phx 16002
 
2.8%
lax 15955
 
2.8%
las 15872
 
2.8%
mco 15125
 
2.7%
sea 12726
 
2.2%
Other values (324) 381051
66.8%
2024-03-30T03:14:55.408838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 194750
 
11.4%
L 158614
 
9.3%
S 146305
 
8.5%
D 132236
 
7.7%
T 91011
 
5.3%
O 86895
 
5.1%
C 85813
 
5.0%
M 78325
 
4.6%
F 70439
 
4.1%
N 66835
 
3.9%
Other values (16) 599959
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 194750
 
11.4%
L 158614
 
9.3%
S 146305
 
8.5%
D 132236
 
7.7%
T 91011
 
5.3%
O 86895
 
5.1%
C 85813
 
5.0%
M 78325
 
4.6%
F 70439
 
4.1%
N 66835
 
3.9%
Other values (16) 599959
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 194750
 
11.4%
L 158614
 
9.3%
S 146305
 
8.5%
D 132236
 
7.7%
T 91011
 
5.3%
O 86895
 
5.1%
C 85813
 
5.0%
M 78325
 
4.6%
F 70439
 
4.1%
N 66835
 
3.9%
Other values (16) 599959
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 194750
 
11.4%
L 158614
 
9.3%
S 146305
 
8.5%
D 132236
 
7.7%
T 91011
 
5.3%
O 86895
 
5.1%
C 85813
 
5.0%
M 78325
 
4.6%
F 70439
 
4.1%
N 66835
 
3.9%
Other values (16) 599959
35.1%
Distinct328
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:56.149368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.057765
Min length8

Characters and Unicode

Total characters7448071
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSt. Louis, MO
2nd rowSyracuse, NY
3rd rowNew York, NY
4th rowKnoxville, TN
5th rowNorfolk, VA
ValueCountFrequency (%)
ca 61806
 
4.6%
tx 60779
 
4.6%
fl 57050
 
4.3%
san 30283
 
2.3%
ga 29904
 
2.2%
ny 29343
 
2.2%
il 28538
 
2.1%
co 28163
 
2.1%
atlanta 28052
 
2.1%
chicago 27498
 
2.1%
Other values (399) 950741
71.4%
2024-03-30T03:14:57.569974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
761763
 
10.2%
a 572465
 
7.7%
, 570394
 
7.7%
o 407456
 
5.5%
e 394552
 
5.3%
n 365701
 
4.9%
t 352660
 
4.7%
l 329834
 
4.4%
i 281897
 
3.8%
r 270296
 
3.6%
Other values (46) 3141053
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7448071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
761763
 
10.2%
a 572465
 
7.7%
, 570394
 
7.7%
o 407456
 
5.5%
e 394552
 
5.3%
n 365701
 
4.9%
t 352660
 
4.7%
l 329834
 
4.4%
i 281897
 
3.8%
r 270296
 
3.6%
Other values (46) 3141053
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7448071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
761763
 
10.2%
a 572465
 
7.7%
, 570394
 
7.7%
o 407456
 
5.5%
e 394552
 
5.3%
n 365701
 
4.9%
t 352660
 
4.7%
l 329834
 
4.4%
i 281897
 
3.8%
r 270296
 
3.6%
Other values (46) 3141053
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7448071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
761763
 
10.2%
a 572465
 
7.7%
, 570394
 
7.7%
o 407456
 
5.5%
e 394552
 
5.3%
n 365701
 
4.9%
t 352660
 
4.7%
l 329834
 
4.4%
i 281897
 
3.8%
r 270296
 
3.6%
Other values (46) 3141053
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:58.148277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.113483
Min length4

Characters and Unicode

Total characters4627882
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissouri
2nd rowNew York
3rd rowNew York
4th rowTennessee
5th rowVirginia
ValueCountFrequency (%)
california 61806
 
9.5%
texas 60779
 
9.3%
florida 57050
 
8.8%
new 43100
 
6.6%
georgia 29904
 
4.6%
york 29343
 
4.5%
illinois 28538
 
4.4%
colorado 28163
 
4.3%
carolina 28114
 
4.3%
north 25038
 
3.8%
Other values (51) 258816
39.8%
2024-03-30T03:14:58.993743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 626472
13.5%
i 521850
 
11.3%
o 452752
 
9.8%
r 342517
 
7.4%
n 336135
 
7.3%
e 278609
 
6.0%
l 262193
 
5.7%
s 256863
 
5.6%
d 121135
 
2.6%
C 119754
 
2.6%
Other values (37) 1309602
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4627882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 626472
13.5%
i 521850
 
11.3%
o 452752
 
9.8%
r 342517
 
7.4%
n 336135
 
7.3%
e 278609
 
6.0%
l 262193
 
5.7%
s 256863
 
5.6%
d 121135
 
2.6%
C 119754
 
2.6%
Other values (37) 1309602
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4627882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 626472
13.5%
i 521850
 
11.3%
o 452752
 
9.8%
r 342517
 
7.4%
n 336135
 
7.3%
e 278609
 
6.0%
l 262193
 
5.7%
s 256863
 
5.6%
d 121135
 
2.6%
C 119754
 
2.6%
Other values (37) 1309602
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4627882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 626472
13.5%
i 521850
 
11.3%
o 452752
 
9.8%
r 342517
 
7.4%
n 336135
 
7.3%
e 278609
 
6.0%
l 262193
 
5.7%
s 256863
 
5.6%
d 121135
 
2.6%
C 119754
 
2.6%
Other values (37) 1309602
28.3%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.891053
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:14:59.455406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median51
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.668717
Coefficient of variation (CV)0.48584817
Kurtosis-1.3177557
Mean54.891053
Median Absolute Deviation (MAD)23
Skewness-0.043748191
Sum31309527
Variance711.22048
MonotonicityNot monotonic
2024-03-30T03:14:59.966905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 61806
 
10.8%
74 60779
 
10.7%
33 57050
 
10.0%
34 29904
 
5.2%
22 29343
 
5.1%
41 28538
 
5.0%
82 28163
 
4.9%
36 23581
 
4.1%
38 18572
 
3.3%
81 18426
 
3.2%
Other values (42) 214232
37.6%
ValueCountFrequency (%)
1 2478
 
0.4%
2 11108
1.9%
3 3412
 
0.6%
4 664
 
0.1%
5 102
 
< 0.1%
11 1671
 
0.3%
12 889
 
0.2%
13 10757
1.9%
14 489
 
0.1%
15 1084
 
0.2%
ValueCountFrequency (%)
93 14953
 
2.6%
92 6327
 
1.1%
91 61806
10.8%
88 817
 
0.1%
87 9845
 
1.7%
86 2219
 
0.4%
85 17543
 
3.1%
84 2010
 
0.4%
83 2364
 
0.4%
82 28163
4.9%

CRS_DEP_TIME
Real number (ℝ)

Distinct1241
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1328.2608
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:00.578143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600
Q1905
median1322
Q31740
95-th percentile2128
Maximum2359
Range2358
Interquartile range (IQR)835

Descriptive statistics

Standard deviation496.32683
Coefficient of variation (CV)0.37366671
Kurtosis-1.0701988
Mean1328.2608
Median Absolute Deviation (MAD)417
Skewness0.074999195
Sum7.5763198 × 108
Variance246340.33
MonotonicityNot monotonic
2024-03-30T03:15:01.131705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 12107
 
2.1%
700 9261
 
1.6%
800 5837
 
1.0%
900 3915
 
0.7%
1000 3738
 
0.7%
630 3355
 
0.6%
730 3240
 
0.6%
615 2869
 
0.5%
830 2835
 
0.5%
1100 2807
 
0.5%
Other values (1231) 520430
91.2%
ValueCountFrequency (%)
1 8
 
< 0.1%
3 2
 
< 0.1%
4 11
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 7
 
< 0.1%
8 7
 
< 0.1%
9 42
< 0.1%
10 1
 
< 0.1%
11 6
 
< 0.1%
ValueCountFrequency (%)
2359 1191
0.2%
2358 63
 
< 0.1%
2357 27
 
< 0.1%
2356 29
 
< 0.1%
2355 247
 
< 0.1%
2354 54
 
< 0.1%
2353 27
 
< 0.1%
2352 18
 
< 0.1%
2351 46
 
< 0.1%
2350 100
 
< 0.1%

DEP_TIME
Real number (ℝ)

Distinct1419
Distinct (%)0.2%
Missing2186
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1330.1195
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:01.712067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile557
Q1908
median1326
Q31744
95-th percentile2135
Maximum2400
Range2399
Interquartile range (IQR)836

Descriptive statistics

Standard deviation506.68853
Coefficient of variation (CV)0.38093458
Kurtosis-1.0174795
Mean1330.1195
Median Absolute Deviation (MAD)418
Skewness0.034564466
Sum7.5578456 × 108
Variance256733.27
MonotonicityNot monotonic
2024-03-30T03:15:02.145917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1555
 
0.3%
557 1390
 
0.2%
556 1385
 
0.2%
558 1336
 
0.2%
559 1228
 
0.2%
554 1218
 
0.2%
655 1204
 
0.2%
600 1164
 
0.2%
658 1105
 
0.2%
657 1101
 
0.2%
Other values (1409) 555522
97.4%
(Missing) 2186
 
0.4%
ValueCountFrequency (%)
1 59
< 0.1%
2 57
< 0.1%
3 53
< 0.1%
4 48
< 0.1%
5 53
< 0.1%
6 54
< 0.1%
7 48
< 0.1%
8 44
< 0.1%
9 48
< 0.1%
10 44
< 0.1%
ValueCountFrequency (%)
2400 50
 
< 0.1%
2359 88
< 0.1%
2358 101
< 0.1%
2357 96
< 0.1%
2356 116
< 0.1%
2355 130
< 0.1%
2354 148
< 0.1%
2353 125
< 0.1%
2352 120
< 0.1%
2351 130
< 0.1%

DEP_DELAY
Real number (ℝ)

ZEROS 

Distinct1072
Distinct (%)0.2%
Missing2199
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean8.2743143
Minimum-99
Maximum3786
Zeros27576
Zeros (%)4.8%
Negative349562
Negative (%)61.3%
Memory size4.4 MiB
2024-03-30T03:15:02.514540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-10
Q1-6
median-2
Q35
95-th percentile58
Maximum3786
Range3885
Interquartile range (IQR)11

Descriptive statistics

Standard deviation47.4198
Coefficient of variation (CV)5.7309644
Kurtosis385.22163
Mean8.2743143
Median Absolute Deviation (MAD)4
Skewness14.04448
Sum4701424
Variance2248.6375
MonotonicityNot monotonic
2024-03-30T03:15:02.889475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 44837
 
7.9%
-4 41881
 
7.3%
-3 40760
 
7.1%
-2 36779
 
6.4%
-6 35988
 
6.3%
-1 32505
 
5.7%
-7 30820
 
5.4%
0 27576
 
4.8%
-8 25115
 
4.4%
-9 18445
 
3.2%
Other values (1062) 233489
40.9%
ValueCountFrequency (%)
-99 1
 
< 0.1%
-59 1
 
< 0.1%
-49 1
 
< 0.1%
-45 2
< 0.1%
-42 1
 
< 0.1%
-40 2
< 0.1%
-39 2
< 0.1%
-38 1
 
< 0.1%
-37 4
< 0.1%
-36 2
< 0.1%
ValueCountFrequency (%)
3786 1
< 0.1%
2915 1
< 0.1%
2604 1
< 0.1%
2495 1
< 0.1%
2413 1
< 0.1%
2395 1
< 0.1%
2298 1
< 0.1%
2025 1
< 0.1%
1897 1
< 0.1%
1813 1
< 0.1%

TAXI_OUT
Real number (ℝ)

Distinct139
Distinct (%)< 0.1%
Missing2242
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean17.413953
Minimum1
Maximum155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:03.241202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q320
95-th percentile33
Maximum155
Range154
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.5711332
Coefficient of variation (CV)0.49219918
Kurtosis12.688846
Mean17.413953
Median Absolute Deviation (MAD)4
Skewness2.5629355
Sum9893772
Variance73.464324
MonotonicityNot monotonic
2024-03-30T03:15:03.601167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 45607
 
8.0%
12 45567
 
8.0%
14 42706
 
7.5%
11 41766
 
7.3%
15 39484
 
6.9%
16 34945
 
6.1%
10 34480
 
6.0%
17 30735
 
5.4%
18 26466
 
4.6%
9 23395
 
4.1%
Other values (129) 203001
35.6%
ValueCountFrequency (%)
1 33
 
< 0.1%
2 22
 
< 0.1%
3 49
 
< 0.1%
4 170
 
< 0.1%
5 520
 
0.1%
6 2329
 
0.4%
7 6198
 
1.1%
8 13007
 
2.3%
9 23395
4.1%
10 34480
6.0%
ValueCountFrequency (%)
155 1
 
< 0.1%
150 1
 
< 0.1%
147 1
 
< 0.1%
144 1
 
< 0.1%
143 3
< 0.1%
140 2
< 0.1%
138 1
 
< 0.1%
137 1
 
< 0.1%
136 1
 
< 0.1%
133 1
 
< 0.1%

TAXI_IN
Real number (ℝ)

Distinct140
Distinct (%)< 0.1%
Missing2506
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean8.0097396
Minimum1
Maximum193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:04.297621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile18
Maximum193
Range192
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.0921938
Coefficient of variation (CV)0.76059824
Kurtosis40.351238
Mean8.0097396
Median Absolute Deviation (MAD)2
Skewness4.1497981
Sum4548635
Variance37.114826
MonotonicityNot monotonic
2024-03-30T03:15:04.702550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 86145
15.1%
5 82438
14.5%
6 67465
11.8%
7 55318
9.7%
3 48547
8.5%
8 42369
7.4%
9 33254
 
5.8%
10 25943
 
4.5%
11 20197
 
3.5%
12 15750
 
2.8%
Other values (130) 90462
15.9%
ValueCountFrequency (%)
1 750
 
0.1%
2 11287
 
2.0%
3 48547
8.5%
4 86145
15.1%
5 82438
14.5%
6 67465
11.8%
7 55318
9.7%
8 42369
7.4%
9 33254
 
5.8%
10 25943
 
4.5%
ValueCountFrequency (%)
193 1
< 0.1%
176 1
< 0.1%
175 1
< 0.1%
172 2
< 0.1%
170 1
< 0.1%
169 1
< 0.1%
165 2
< 0.1%
163 1
< 0.1%
162 1
< 0.1%
161 1
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1358
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1490.2792
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:05.113968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile720
Q11102
median1518
Q31928
95-th percentile2259
Maximum2359
Range2358
Interquartile range (IQR)826

Descriptive statistics

Standard deviation525.82911
Coefficient of variation (CV)0.35283933
Kurtosis-0.42972861
Mean1490.2792
Median Absolute Deviation (MAD)413
Skewness-0.31775724
Sum8.5004631 × 108
Variance276496.25
MonotonicityNot monotonic
2024-03-30T03:15:05.490583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 2439
 
0.4%
2200 2133
 
0.4%
1640 1790
 
0.3%
1855 1756
 
0.3%
1845 1735
 
0.3%
1810 1732
 
0.3%
905 1653
 
0.3%
1000 1653
 
0.3%
1940 1618
 
0.3%
1710 1589
 
0.3%
Other values (1348) 552296
96.8%
ValueCountFrequency (%)
1 66
 
< 0.1%
2 119
 
< 0.1%
3 191
 
< 0.1%
4 191
 
< 0.1%
5 472
0.1%
6 167
 
< 0.1%
7 129
 
< 0.1%
8 139
 
< 0.1%
9 142
 
< 0.1%
10 492
0.1%
ValueCountFrequency (%)
2359 2439
0.4%
2358 637
 
0.1%
2357 850
 
0.1%
2356 498
 
0.1%
2355 1201
0.2%
2354 488
 
0.1%
2353 432
 
0.1%
2352 469
 
0.1%
2351 305
 
0.1%
2350 689
 
0.1%

ARR_TIME
Real number (ℝ)

Distinct1440
Distinct (%)0.3%
Missing2506
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1469.0953
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:05.861624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile652
Q11049
median1508
Q31921
95-th percentile2253
Maximum2400
Range2399
Interquartile range (IQR)872

Descriptive statistics

Standard deviation539.33587
Coefficient of variation (CV)0.3671211
Kurtosis-0.38442734
Mean1469.0953
Median Absolute Deviation (MAD)423
Skewness-0.36349995
Sum8.3428157 × 108
Variance290883.18
MonotonicityNot monotonic
2024-03-30T03:15:06.583744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1656 654
 
0.1%
1628 647
 
0.1%
1703 644
 
0.1%
1634 637
 
0.1%
1607 637
 
0.1%
1145 635
 
0.1%
1635 633
 
0.1%
2004 632
 
0.1%
1142 631
 
0.1%
1643 629
 
0.1%
Other values (1430) 561509
98.4%
(Missing) 2506
 
0.4%
ValueCountFrequency (%)
1 352
0.1%
2 329
0.1%
3 332
0.1%
4 281
< 0.1%
5 293
0.1%
6 306
0.1%
7 295
0.1%
8 304
0.1%
9 282
< 0.1%
10 262
< 0.1%
ValueCountFrequency (%)
2400 307
0.1%
2359 329
0.1%
2358 317
0.1%
2357 340
0.1%
2356 373
0.1%
2355 367
0.1%
2354 372
0.1%
2353 395
0.1%
2352 412
0.1%
2351 407
0.1%

ARR_DELAY
Real number (ℝ)

ZEROS 

Distinct1102
Distinct (%)0.2%
Missing3453
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.50933166
Minimum-98
Maximum3795
Zeros10088
Zeros (%)1.8%
Negative381564
Negative (%)66.9%
Memory size4.4 MiB
2024-03-30T03:15:06.955673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-98
5-th percentile-31
Q1-17
median-8
Q35
95-th percentile54
Maximum3795
Range3893
Interquartile range (IQR)22

Descriptive statistics

Standard deviation49.070609
Coefficient of variation (CV)96.343136
Kurtosis338.93037
Mean0.50933166
Median Absolute Deviation (MAD)11
Skewness12.688883
Sum288761
Variance2407.9247
MonotonicityNot monotonic
2024-03-30T03:15:07.335309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-13 16059
 
2.8%
-12 15909
 
2.8%
-11 15818
 
2.8%
-10 15598
 
2.7%
-14 15539
 
2.7%
-9 15417
 
2.7%
-8 15162
 
2.7%
-15 14936
 
2.6%
-16 14459
 
2.5%
-7 14403
 
2.5%
Other values (1092) 413641
72.5%
ValueCountFrequency (%)
-98 1
 
< 0.1%
-97 1
 
< 0.1%
-96 1
 
< 0.1%
-91 1
 
< 0.1%
-88 1
 
< 0.1%
-86 3
< 0.1%
-85 1
 
< 0.1%
-83 1
 
< 0.1%
-82 4
< 0.1%
-81 1
 
< 0.1%
ValueCountFrequency (%)
3795 1
< 0.1%
2924 1
< 0.1%
2604 1
< 0.1%
2484 1
< 0.1%
2427 1
< 0.1%
2403 1
< 0.1%
2289 1
< 0.1%
2028 1
< 0.1%
1873 1
< 0.1%
1812 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0.0
568084 
1.0
 
2310

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1711182
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 568084
99.6%
1.0 2310
 
0.4%

Length

2024-03-30T03:15:07.704854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:15:07.965447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 568084
99.6%
1.0 2310
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1138478
66.5%
. 570394
33.3%
1 2310
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1138478
66.5%
. 570394
33.3%
1 2310
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1138478
66.5%
. 570394
33.3%
1 2310
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1138478
66.5%
. 570394
33.3%
1 2310
 
0.1%

CANCELLATION_CODE
Categorical

MISSING 

Distinct4
Distinct (%)0.2%
Missing568084
Missing (%)99.6%
Memory size4.4 MiB
B
1392 
A
789 
C
 
127
D
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2310
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
B 1392
 
0.2%
A 789
 
0.1%
C 127
 
< 0.1%
D 2
 
< 0.1%
(Missing) 568084
99.6%

Length

2024-03-30T03:15:08.351895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:15:08.657665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
b 1392
60.3%
a 789
34.2%
c 127
 
5.5%
d 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
B 1392
60.3%
A 789
34.2%
C 127
 
5.5%
D 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 1392
60.3%
A 789
34.2%
C 127
 
5.5%
D 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 1392
60.3%
A 789
34.2%
C 127
 
5.5%
D 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 1392
60.3%
A 789
34.2%
C 127
 
5.5%
D 2
 
0.1%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0.0
569251 
1.0
 
1143

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1711182
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 569251
99.8%
1.0 1143
 
0.2%

Length

2024-03-30T03:15:08.946566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:15:09.367943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 569251
99.8%
1.0 1143
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1139645
66.6%
. 570394
33.3%
1 1143
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1139645
66.6%
. 570394
33.3%
1 1143
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1139645
66.6%
. 570394
33.3%
1 1143
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1711182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1139645
66.6%
. 570394
33.3%
1 1143
 
0.1%

AIR_TIME
Real number (ℝ)

Distinct635
Distinct (%)0.1%
Missing3453
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean116.76931
Minimum6
Maximum672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:09.673870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile36
Q165
median101
Q3147
95-th percentile271
Maximum672
Range666
Interquartile range (IQR)82

Descriptive statistics

Standard deviation70.76748
Coefficient of variation (CV)0.60604518
Kurtosis2.6326529
Mean116.76931
Median Absolute Deviation (MAD)40
Skewness1.407162
Sum66201311
Variance5008.0362
MonotonicityNot monotonic
2024-03-30T03:15:10.052829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 4714
 
0.8%
63 4621
 
0.8%
66 4590
 
0.8%
62 4528
 
0.8%
64 4524
 
0.8%
60 4485
 
0.8%
65 4459
 
0.8%
67 4444
 
0.8%
56 4395
 
0.8%
55 4390
 
0.8%
Other values (625) 521791
91.5%
ValueCountFrequency (%)
6 1
 
< 0.1%
8 7
 
< 0.1%
9 15
 
< 0.1%
10 17
 
< 0.1%
11 3
 
< 0.1%
12 4
 
< 0.1%
13 5
 
< 0.1%
14 11
 
< 0.1%
15 19
 
< 0.1%
16 66
< 0.1%
ValueCountFrequency (%)
672 1
< 0.1%
670 1
< 0.1%
668 1
< 0.1%
663 1
< 0.1%
661 1
< 0.1%
660 1
< 0.1%
657 1
< 0.1%
656 1
< 0.1%
655 1
< 0.1%
654 2
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct784
Distinct (%)0.9%
Missing481241
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean23.501385
Minimum0
Maximum3786
Zeros39145
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:10.553620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q321
95-th percentile96
Maximum3786
Range3786
Interquartile range (IQR)21

Descriptive statistics

Standard deviation73.723444
Coefficient of variation (CV)3.1369829
Kurtosis256.17866
Mean23.501385
Median Absolute Deviation (MAD)4
Skewness12.01631
Sum2095219
Variance5435.1463
MonotonicityNot monotonic
2024-03-30T03:15:10.880155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39145
 
6.9%
1 1736
 
0.3%
6 1669
 
0.3%
2 1640
 
0.3%
3 1631
 
0.3%
4 1622
 
0.3%
15 1562
 
0.3%
5 1528
 
0.3%
7 1511
 
0.3%
16 1432
 
0.3%
Other values (774) 35677
 
6.3%
(Missing) 481241
84.4%
ValueCountFrequency (%)
0 39145
6.9%
1 1736
 
0.3%
2 1640
 
0.3%
3 1631
 
0.3%
4 1622
 
0.3%
5 1528
 
0.3%
6 1669
 
0.3%
7 1511
 
0.3%
8 1402
 
0.2%
9 1375
 
0.2%
ValueCountFrequency (%)
3786 1
< 0.1%
2604 1
< 0.1%
2484 1
< 0.1%
2403 1
< 0.1%
2289 1
< 0.1%
2033 1
< 0.1%
2025 1
< 0.1%
1873 1
< 0.1%
1812 1
< 0.1%
1728 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct517
Distinct (%)0.6%
Missing481241
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean3.9403834
Minimum0
Maximum1529
Zeros85878
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:11.251174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1529
Range1529
Interquartile range (IQR)0

Descriptive statistics

Standard deviation42.071916
Coefficient of variation (CV)10.677112
Kurtosis381.62417
Mean3.9403834
Median Absolute Deviation (MAD)0
Skewness17.789533
Sum351297
Variance1770.0461
MonotonicityNot monotonic
2024-03-30T03:15:11.664565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85878
 
15.1%
16 73
 
< 0.1%
3 72
 
< 0.1%
4 72
 
< 0.1%
2 66
 
< 0.1%
15 63
 
< 0.1%
9 63
 
< 0.1%
19 62
 
< 0.1%
7 58
 
< 0.1%
20 57
 
< 0.1%
Other values (507) 2689
 
0.5%
(Missing) 481241
84.4%
ValueCountFrequency (%)
0 85878
15.1%
1 46
 
< 0.1%
2 66
 
< 0.1%
3 72
 
< 0.1%
4 72
 
< 0.1%
5 50
 
< 0.1%
6 52
 
< 0.1%
7 58
 
< 0.1%
8 49
 
< 0.1%
9 63
 
< 0.1%
ValueCountFrequency (%)
1529 1
< 0.1%
1487 1
< 0.1%
1361 1
< 0.1%
1355 1
< 0.1%
1352 1
< 0.1%
1247 1
< 0.1%
1202 2
< 0.1%
1195 1
< 0.1%
1181 1
< 0.1%
1169 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct306
Distinct (%)0.3%
Missing481241
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean11.146546
Minimum0
Maximum1260
Zeros44910
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:12.032723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile45
Maximum1260
Range1260
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.697448
Coefficient of variation (CV)2.2157042
Kurtosis305.1785
Mean11.146546
Median Absolute Deviation (MAD)0
Skewness10.812329
Sum993748
Variance609.96393
MonotonicityNot monotonic
2024-03-30T03:15:12.484661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44910
 
7.9%
1 2812
 
0.5%
15 2116
 
0.4%
16 1810
 
0.3%
2 1727
 
0.3%
3 1612
 
0.3%
17 1608
 
0.3%
18 1507
 
0.3%
4 1504
 
0.3%
5 1454
 
0.3%
Other values (296) 28093
 
4.9%
(Missing) 481241
84.4%
ValueCountFrequency (%)
0 44910
7.9%
1 2812
 
0.5%
2 1727
 
0.3%
3 1612
 
0.3%
4 1504
 
0.3%
5 1454
 
0.3%
6 1372
 
0.2%
7 1313
 
0.2%
8 1209
 
0.2%
9 1200
 
0.2%
ValueCountFrequency (%)
1260 1
< 0.1%
1148 1
< 0.1%
1134 1
< 0.1%
1026 1
< 0.1%
828 1
< 0.1%
800 1
< 0.1%
764 1
< 0.1%
704 1
< 0.1%
656 1
< 0.1%
629 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct98
Distinct (%)0.1%
Missing481241
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean0.21174834
Minimum0
Maximum276
Zeros88394
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:12.794214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum276
Range276
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.511695
Coefficient of variation (CV)16.584286
Kurtosis1692.179
Mean0.21174834
Median Absolute Deviation (MAD)0
Skewness34.049075
Sum18878
Variance12.332002
MonotonicityNot monotonic
2024-03-30T03:15:13.109824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88394
 
15.5%
8 36
 
< 0.1%
12 31
 
< 0.1%
17 29
 
< 0.1%
15 29
 
< 0.1%
16 27
 
< 0.1%
7 26
 
< 0.1%
6 25
 
< 0.1%
14 25
 
< 0.1%
20 23
 
< 0.1%
Other values (88) 508
 
0.1%
(Missing) 481241
84.4%
ValueCountFrequency (%)
0 88394
15.5%
1 11
 
< 0.1%
2 15
 
< 0.1%
3 17
 
< 0.1%
4 13
 
< 0.1%
5 21
 
< 0.1%
6 25
 
< 0.1%
7 26
 
< 0.1%
8 36
 
< 0.1%
9 22
 
< 0.1%
ValueCountFrequency (%)
276 1
< 0.1%
242 1
< 0.1%
227 1
< 0.1%
223 1
< 0.1%
187 1
< 0.1%
169 1
< 0.1%
162 1
< 0.1%
157 1
< 0.1%
156 1
< 0.1%
155 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct614
Distinct (%)0.7%
Missing481241
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean24.583514
Minimum0
Maximum2586
Zeros45982
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:15:13.462894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q327
95-th percentile109
Maximum2586
Range2586
Interquartile range (IQR)27

Descriptive statistics

Standard deviation58.69079
Coefficient of variation (CV)2.3874044
Kurtosis136.08824
Mean24.583514
Median Absolute Deviation (MAD)0
Skewness8.2164906
Sum2191694
Variance3444.6088
MonotonicityNot monotonic
2024-03-30T03:15:13.820997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45982
 
8.1%
16 1149
 
0.2%
15 1145
 
0.2%
17 1101
 
0.2%
18 1004
 
0.2%
20 929
 
0.2%
19 910
 
0.2%
13 828
 
0.1%
21 817
 
0.1%
12 805
 
0.1%
Other values (604) 34483
 
6.0%
(Missing) 481241
84.4%
ValueCountFrequency (%)
0 45982
8.1%
1 545
 
0.1%
2 616
 
0.1%
3 602
 
0.1%
4 608
 
0.1%
5 633
 
0.1%
6 676
 
0.1%
7 664
 
0.1%
8 707
 
0.1%
9 715
 
0.1%
ValueCountFrequency (%)
2586 1
< 0.1%
1609 1
< 0.1%
1606 1
< 0.1%
1424 1
< 0.1%
1297 1
< 0.1%
1261 1
< 0.1%
1233 1
< 0.1%
1222 1
< 0.1%
1215 1
< 0.1%
1210 1
< 0.1%

Interactions

2024-03-30T03:14:26.273009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:16.919675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:23.897844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:30.857511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:38.199134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:44.497411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:51.351560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:58.252313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:05.314093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:12.809562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:20.737115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:28.949569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:36.268772image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:42.953404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:49.545783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:56.769813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:03.485388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:09.443381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:15.105147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:20.575574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:26.533533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:17.461471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:24.305633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:31.213000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:38.518293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:44.816952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:51.723484image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:58.589429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:05.903242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:13.153948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:21.261722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:29.292779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:36.658012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:43.303688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:49.944408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:57.118021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:03.776325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:09.708151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:15.341903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:20.876047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:26.781032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:17.767194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:24.645032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:31.532196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:38.827157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:45.153095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:52.072516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:58.902134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:06.504890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:13.482923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:21.765845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:29.621172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:37.017617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:43.622437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:50.382148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:57.603951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:04.086591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:09.999524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:15.597547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:21.135311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:27.063284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:18.108791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:24.984211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:31.831751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:39.152991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:45.495011image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:52.509379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:59.231266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:07.059622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:13.846745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:22.400030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:29.992661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:37.366172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:43.967757image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:50.768152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:57.992516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:04.442401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:10.317042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:15.847840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:21.470377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:27.288703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:18.440330image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:25.292754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:32.200008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:39.447340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:45.982337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:52.834967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:59.556115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:07.531558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:14.215938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:22.771619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:30.370275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:37.817769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:44.313086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:51.149077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:58.356604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:04.738048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:10.590351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:16.126056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:21.743148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:27.585411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:18.753544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:25.608369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:32.540041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:39.786137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:46.366361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:53.192814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:59.908780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:07.899281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:14.571585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:23.189443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:30.743391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:38.194587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:44.657183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:51.540706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:58.729768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:05.052875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:10.904497image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:16.407608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:22.077776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:28.310110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:19.041634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:25.893057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:32.834424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:40.112859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:46.678451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:53.528750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:00.252673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:08.260607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:14.894599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:23.582237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:31.076720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:38.491834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:44.977611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:51.914778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:59.067193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:05.329128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:14:08.354564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:14.015864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:19.312386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:25.196412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:31.665640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:22.931109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:29.960110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:37.218898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:43.601559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:50.371985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:57.339116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:04.092439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:11.920663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:19.389228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:27.818854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:35.226137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:42.097695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:48.736751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:55.821212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:02.742220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:08.650917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:14.310393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:19.594492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:25.476482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:31.974338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:23.224744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:30.250477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:37.511175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:43.880652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:50.656092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:57.598492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:04.428522image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:12.219709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:19.671331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:28.301247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:35.527661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:42.369179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:48.976157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:56.127208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:02.994175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:08.924488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:14.578876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:19.832683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:25.730119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:32.275048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:23.517197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:30.508578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:37.785254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:44.156986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:50.952715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:12:57.846041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:04.711112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:12.460164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:20.150029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:28.567446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:35.819899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:42.613980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:49.214069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:13:56.401943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:03.239300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:09.171235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:14.815833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:20.132122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:14:25.999150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T03:14:32.980417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T03:14:36.555368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
0112/4/2023 12:00:00 AM9E480012953LGANew York, NYNew York2215016STLSt. Louis, MOMissouri6418461839.0-7.038.08.020462046.00.00.0NaN0.0141.0NaNNaNNaNNaNNaN
1112/4/2023 12:00:00 AM9E480312953LGANew York, NYNew York2215096SYRSyracuse, NYNew York2221522144.0-8.014.019.023032301.0-2.00.0NaN0.044.0NaNNaNNaNNaNNaN
2112/4/2023 12:00:00 AM9E480415412TYSKnoxville, TNTennessee5412953LGANew York, NYNew York22647642.0-5.012.07.0857822.0-35.00.0NaN0.081.0NaNNaNNaNNaNNaN
3112/4/2023 12:00:00 AM9E480512953LGANew York, NYNew York2215412TYSKnoxville, TNTennessee5418001754.0-6.013.04.020281951.0-37.00.0NaN0.0100.0NaNNaNNaNNaNNaN
4112/4/2023 12:00:00 AM9E480812953LGANew York, NYNew York2213931ORFNorfolk, VAVirginia3815291523.0-6.018.03.017091639.0-30.00.0NaN0.055.0NaNNaNNaNNaNNaN
5112/4/2023 12:00:00 AM9E480813931ORFNorfolk, VAVirginia3812953LGANew York, NYNew York2218151810.0-5.011.07.019571926.0-31.00.0NaN0.058.0NaNNaNNaNNaNNaN
6112/4/2023 12:00:00 AM9E481010397ATLAtlanta, GAGeorgia3415919XNAFayetteville, ARArkansas71955951.0-4.014.06.010531048.0-5.00.0NaN0.097.0NaNNaNNaNNaNNaN
7112/4/2023 12:00:00 AM9E481113198MCIKansas City, MOMissouri6412478JFKNew York, NYNew York22704700.0-4.023.08.010591056.0-3.00.0NaN0.0145.0NaNNaNNaNNaNNaN
8112/4/2023 12:00:00 AM9E481212953LGANew York, NYNew York2213577MYRMyrtle Beach, SCSouth Carolina3720402035.0-5.015.03.022542222.0-32.00.0NaN0.089.0NaNNaNNaNNaNNaN
9112/4/2023 12:00:00 AM9E481312478JFKNew York, NYNew York2213931ORFNorfolk, VAVirginia3813401329.0-11.035.04.015101509.0-1.00.0NaN0.061.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
570384712/31/2023 12:00:00 AMYX580213342MKEMilwaukee, WIWisconsin4510721BOSBoston, MAMassachusetts1317151705.0-10.028.06.020362034.0-2.00.0NaN0.0115.0NaNNaNNaNNaNNaN
570385712/31/2023 12:00:00 AMYX580711066CMHColumbus, OHOhio4412953LGANew York, NYNew York22800753.0-7.015.010.0956935.0-21.00.0NaN0.077.0NaNNaNNaNNaNNaN
570386712/31/2023 12:00:00 AMYX581011066CMHColumbus, OHOhio4410721BOSBoston, MAMassachusetts13950941.0-9.010.07.011501127.0-23.00.0NaN0.089.0NaNNaNNaNNaNNaN
570387712/31/2023 12:00:00 AMYX581314122PITPittsburgh, PAPennsylvania2311433DTWDetroit, MIMichigan43620614.0-6.016.07.0749715.0-34.00.0NaN0.038.0NaNNaNNaNNaNNaN
570388712/31/2023 12:00:00 AMYX581612953LGANew York, NYNew York2210721BOSBoston, MAMassachusetts1312001211.011.016.08.013251317.0-8.00.0NaN0.042.0NaNNaNNaNNaNNaN
570389712/31/2023 12:00:00 AMYX581710721BOSBoston, MAMassachusetts1312953LGANew York, NYNew York2212001152.0-8.013.06.013281249.0-39.00.0NaN0.038.0NaNNaNNaNNaNNaN
570390712/31/2023 12:00:00 AMYX581810693BNANashville, TNTennessee5412953LGANew York, NYNew York22740733.0-7.032.08.011081104.0-4.00.0NaN0.0111.0NaNNaNNaNNaNNaN
570391712/31/2023 12:00:00 AMYX583712953LGANew York, NYNew York2210693BNANashville, TNTennessee5414201412.0-8.015.011.016031535.0-28.00.0NaN0.0117.0NaNNaNNaNNaNNaN
570392712/31/2023 12:00:00 AMYX584413485MSNMadison, WIWisconsin4512953LGANew York, NYNew York221000951.0-9.030.010.013241322.0-2.00.0NaN0.0111.0NaNNaNNaNNaNNaN
570393712/31/2023 12:00:00 AMYX584511057CLTCharlotte, NCNorth Carolina3612953LGANew York, NYNew York22700658.0-2.027.07.0908848.0-20.00.0NaN0.076.0NaNNaNNaNNaNNaN